基于 GCN-LSTM 模型的宠物产品销量预测

With the rapid growth of the pet market, accurately predicting the sales of pet products is critical to the operation and decision-making of businesses. Traditional sales forecasting methods often struggle to fully capture the spatiotemporal characteristics of pet product sales data. In this paper, we propose a model based on Graph Convolutional Network (GCN) and Long Short-Term Memory Network (LSTM) for predicting the sales of pet products. The model combines the extraction ability of GCN for spatial features and the processing ability of LSTM for time series features, which can effectively mine the spatiotemporal dependencies in pet product sales data. Through the experimental verification of the real data of the e-commerce platform, the model is better than the traditional prediction model in terms of prediction accuracy, which provides more reliable support for the sales management of pet products.